Putting the behavior into animal movement modeling: Improved activity budgets from use of ancillary tag information

• Published in: Ecology and Evolution
• Main Authors: Bestley, Sophie, Jonsen, Ian, Harcourt, Robert G., Hindell, Mark A., Gales, Nicholas J.
• Format: Text
• Language: English
• Published: John Wiley and Sons Inc. 2016
• Subjects: Original Research; Antarctic; The Antarctic; Weddell; Antarc*; Antarctic Fur Seal; Antarctic Fur Seals; Weddell Seal; Weddell Seals
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5108274/; http://www.ncbi.nlm.nih.gov/pubmed/27878092; https://doi.org/10.1002/ece3.2530

Animal movement research relies on biotelemetry, and telemetry‐based locations are increasingly augmented with ancillary information. This presents an underutilized opportunity to enhance movement process models. Given tags designed to record specific behaviors, efforts are increasing to update movement models beyond reliance solely upon horizontal movement information to improve inference of space use and activity budgets. We present two state‐space models adapted to incorporate ancillary data to inform three discrete movement states: directed, resident, and an activity state. These were developed for two case studies: (1) a “haulout” model for Weddell seals, and (2) an “activity” model for Antarctic fur seals which intersperse periods of diving activity and inactivity. The methodology is easily implementable with any ancillary data that can be expressed as a proportion (or binary) indicator. A comparison of the models augmented with ancillary information and unaugmented models confirmed that many behavioral states appeared mischaracterized in the latter. Important changes in subsequent activity budgets occurred. Haulout accounted for 0.17 of the overall Weddell seal time budget, with the estimated proportion of time spent in a resident state reduced from a posterior median of 0.69 (0.65–0.73; 95% HPDI) to 0.54 (0.50–0.58 HPDI). The drop was more dramatic in the Antarctic fur seal case, from 0.57 (0.52–0.63 HPDI) to 0.22 (0.20–0.25 HPDI), with 0.35 (0.31–0.39 HPDI) of time spent in the inactive (nondiving) state. These findings reinforce previously raised contentions about the drawbacks of behavioral states inferred solely from horizontal movements. Our findings have implications for assessing habitat requirements; estimating energetics and consumption; and management efforts such as mitigating fisheries interactions. Combining multiple sources of information within integrated frameworks should improve inference of relationships between movement decisions and fitness, the interplay between resource and habitat ...